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Wee-Chong Oon

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

IJCAI Conference 2011 Conference Paper

Space Defragmentation Heuristic for 2D and 3D Bin Packing Problems

Zhaoyi Zhang
Songshan Guo
Wenbin Zhu
Wee-Chong Oon
Andrew Lim

One of main difficulties of multi-dimensional packing problems is the fragmentation of free space into several unusable small parts after a few items are packed. This study proposes a defragmentation technique to combine the fragmented space into a continuous usable space, which potentially allows the packing of additional items. We illustrate the effectiveness of this technique on the two- and three-dimensional Bin Packing Problems. In conjunction with a bin shuffling strategy for incremental improvement, our resultant algorithm outperforms all leading meta-heuristic approaches.

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AAAI Conference 2010 Conference Paper

The Tree Representation of Feasible Solutions for the TSP with Pickup and Delivery and LIFO Loading

Dejian Tu
Songshan Guo
Hu Qin
Wee-Chong Oon
Andrew Lim

The feasible solutions of the traveling salesman problem with pickup and delivery (TSPPD) are represented by vertex lists in existing literature. However, when the TSPPD requires that the loading and unloading operations must be performed in a last-in-first-out (LIFO) manner, we show that its feasible solutions can be represented by trees. Consequently, we develop a variable neighbourhood search (VNS) heuristic for the TSPPD with last-in-first-out loading (TSPPDL) involving several search operators based on the tree data structure. Experiments show that our VNS heuristic is superior to the current best heuristics for TSPPDL in terms of both solution quality and computing time.

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AAAI Conference 2007 Conference Paper

M2ICAL Analyses HC-Gammon

Wee-Chong Oon

We analyse Pollack and Blair’s HC-Gammon backgammon program using a new technique that performs Monte Carlo simulations to derive a Markov Chain model for Imperfect Comparison ALgorithms, called the M2 ICAL method, which models the behavior of the algorithm using a Markov chain, each of whose states represents a class of players of similar strength. The Markov chain transition matrix is populated using Monte Carlo simulations. Once generated, the matrix allows fairly accurate predictions of the expected solution quality, standard deviation and time to convergence of the algorithm. This allows us to make some observations on the validity of Pollack and Blair’s conclusions, and also shows the application of the M2 ICAL method on a previously published work.

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